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Spark: modular spiking neural networks [PDF]

Frontiers in Artificial Intelligence
Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy.
Mario Franco, Carlos Gershenson
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Accelerating spiking neural networks with photonic reconfigurable devices [PDF]

Nature Communications
Spiking neural networks face hardware limitations as conventional architectures exhibit low array utilization, underperforming GPU-driven artificial neural networks in vision tasks. We present a programmable spiking neurocomputing architecture using CMOS-
Chen Lu, Kangli Xu, Jiaming Liu, Yuanhong Ding, Jiajie Yu, Jieru Song, Jialin Meng, Tianyu Wang, Shuming Guo, Xingcheng Jin, Hao Zhu, Qingqing Sun, David Wei Zhang, Ningsheng Xu, Yibo Fan, Lin Chen   +15 more
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Spiking Neural Networks and Their Applications: A Review

Brain Sciences, 2022
The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs.
Kashu Yamazaki, Viet-Khoa Vo-Ho, Darshan Bulsara, Ngan Le   +3 more
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Federated training of spiking neural networks on edge hardware for audio processing [PDF]

Frontiers in Neuroscience
Spiking Neural Networks have caught significant attention recently for their potential for energy-efficient computation on neuromorphic hardware and their event-driven processing.
Swaroop S. Kaimal, Ashwin JB, S. Sofana Reka, Prakash Venugopal   +3 more
doaj   +2 more sources

Efficient event-based delay learning in spiking neural networks [PDF]

Nature Communications
Spiking Neural Networks compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks.
Balázs Mészáros, James C. Knight, Thomas Nowotny   +2 more
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Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks [PDF]

Scientific Reports
Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI).
Aaditya Joshi, Paramveer Singh Matharu, Lokesh Malviya, Manoj Kumar, Akshay Jadhav   +4 more
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BIASNN: a biologically inspired attention mechanism in spiking neural networks for image classification [PDF]

Scientific Reports
Spiking Neural Networks (SNNs), designed to more accurately model the brain’s neurobiological processes, have been proposed as energy-efficient alternatives to conventional Artificial Neural Networks (ANNs), which typically incur high computational and ...
Kevin Takala, Wachirawut Thamviset, Sartra Wongthanavasu   +2 more
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Spiking Neural Network Model for Brain-like Computing and Progress of Its Learning Algorithm [PDF]

Jisuanji kexue, 2023
With the increasingly prominent limitations of deep neural networks in practical applications,brain-like computing spiking neural networks with biological interpretability have become the focus of research.The uncertainty and complex diversity of ...
HUANG Zenan, LIU Xiaojie, ZHAO Chenhui, DENG Yabin, GUO Donghui
doaj   +1 more source

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework

Molecules, 2023
Spiking neural networks are biologically inspired machine learning algorithms attracting researchers’ attention for their applicability to alternative energy-efficient hardware other than traditional computers.
Mauro Nascimben, Lia Rimondini
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Exploring the Connection Between Binary and Spiking Neural Networks

Frontiers in Neuroscience, 2020
On-chip edge intelligence has necessitated the exploration of algorithmic techniques to reduce the compute requirements of current machine learning frameworks.
Sen Lu, Abhronil Sengupta
doaj   +1 more source